{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-22T15:07:23.051649Z",
     "start_time": "2025-04-22T15:07:22.078909Z"
    }
   },
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "\n",
    "class bayes_model():\n",
    "    def __int__(self):\n",
    "        pass\n",
    "    def load_data(self):\n",
    "        data = datasets.load_iris()\n",
    "        iris_target = data.target\n",
    "        iris_features = pd.DataFrame(data=data.data, columns=data.feature_names)\n",
    "        train_x, test_x, train_y, test_y = train_test_split(iris_features, iris_target, test_size=0.3, random_state=123)\n",
    "        return train_x, test_x, train_y, test_y\n",
    "    def train_model(self, train_x, train_y):\n",
    "        clf = GaussianNB()\n",
    "        clf.fit(train_x, train_y)\n",
    "        return clf\n",
    "    def proba_data(self, clf, test_x, test_y):\n",
    "        y_predict = clf.predict(test_x)\n",
    "        y_proba = clf.predict_proba(test_x)\n",
    "        accuracy = metrics.accuracy_score(test_y, y_predict) * 100\n",
    "        tot1 = pd.DataFrame([test_y, y_predict]).T\n",
    "        tot2 = pd.DataFrame(y_proba).applymap(lambda x: '%.2f' % x)\n",
    "        tot = pd.merge(tot1, tot2, left_index=True, right_index=True)\n",
    "        tot.columns=['y_true', 'y_predict', 'predict_0', 'predict_1', 'predict_2']\n",
    "        print('The accuracy of Testset is: %d%%' % (accuracy))\n",
    "        print('The result of predict is: \\n', tot.head())\n",
    "        return accuracy, tot\n",
    "    def exc_p(self):\n",
    "        train_x, test_x, train_y, test_y = self.load_data()\n",
    "        clf = self.train_model(train_x, train_y)\n",
    "        res = self.proba_data(clf, test_x, test_y)\n",
    "        return res\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    bayes_model().exc_p()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Testset is: 95%\n",
      "The result of predict is: \n",
      "    y_true  y_predict predict_0 predict_1 predict_2\n",
      "0       1          1      0.00      0.95      0.05\n",
      "1       2          2      0.00      0.00      1.00\n",
      "2       2          2      0.00      0.00      1.00\n",
      "3       1          1      0.00      1.00      0.00\n",
      "4       0          0      1.00      0.00      0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_20784\\4028432557.py:27: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  tot2 = pd.DataFrame(y_proba).applymap(lambda x: '%.2f' % x)\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-22T15:07:23.536014Z",
     "start_time": "2025-04-22T15:07:23.156279Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import beta\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']      # 中文字体\n",
    "plt.rcParams['axes.unicode_minus'] = False        # 正常显示负号\n",
    "# 观测数据：7次正面，3次反面\n",
    "heads = 7\n",
    "tails = 3\n",
    "\n",
    "# 先验参数：Uniform(0,1) = Beta(1,1)\n",
    "alpha_prior = 1\n",
    "beta_prior = 1\n",
    "\n",
    "# 后验参数更新（Beta分布）\n",
    "alpha_post = alpha_prior + heads\n",
    "beta_post = beta_prior + tails\n",
    "\n",
    "# 创建p的取值空间\n",
    "p = np.linspace(0, 1, 1000)\n",
    "posterior = beta.pdf(p, alpha_post, beta_post)\n",
    "\n",
    "# 可视化\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(p, posterior, label=f'Posterior: Beta({alpha_post}, {beta_post})', color='royalblue')\n",
    "plt.axvline(p[np.argmax(posterior)], color='red', linestyle='--', label=f'MAP ≈ {p[np.argmax(posterior)]:.2f}')\n",
    "plt.title('后验概率分布：硬币正面概率的贝叶斯估计')\n",
    "plt.xlabel('p（硬币正面概率）')\n",
    "plt.ylabel('后验密度')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "id": "c904e9415f9d0255",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-22T15:07:24.697071Z",
     "start_time": "2025-04-22T15:07:24.693991Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "9faa0fa63d8a4ffd",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-22T15:07:24.710927Z",
     "start_time": "2025-04-22T15:07:24.708562Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "1dd600e29e7122e1",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
